Biomarkers useful for detection of types, grades and stages of human breast cancer

ABSTRACT

The present invention relates to biomarkers useful for detection of types, grades and stages of human breast cancer. The present invention particularly relates to the development of these identified biomarkers as a miRNA chip for the early and accurate diagnosis of human breast cancer. This patent application highlights the novelty in the utility of these miRNAs, that they could be used as a diagnostic kit (miRNA chip) for early and accurate detection of breast cancer grades, stages and subtypes. Few to hundreds of samples can be checked within a span of 2 to 3 hrs and hence this becomes an easy, fast, robust and high throughput technology for screening program for early detection of breast cancer.

PRIORITY CLAIM TO RELATED APPLICATIONS

This application is a U.S. national stage application filed under 35 U.S.C. § 371 from International Application Serial No. PCT/IB2012/002090, which was filed Oct. 17, 2012, and published as WO 2013/057567 on Apr. 25, 2013, and which claims priority to India Application No. 1142/DEL/2011, filed Oct. 19, 2011, which applications and publication are incorporated by reference as if reproduced herein and made a part hereof in their entirety, and the benefit of priority of each of which is claimed herein.

FIELD OF THE INVENTION

The present invention relates to a panel of biomarkers useful for detection of types, grades and stages of human breast cancer. The present invention particularly relates to the development of these identified biomarkers as a miRNA chip for the early and accurate diagnosis of human breast cancer. This patent application highlights the novelty in the utility of these miRNAs, that they could be used as a diagnostic kit (miRNA chip) for early and accurate detection of breast cancer grades, stages and subtypes.

BACKGROUND OF THE INVENTION AND DESCRIPTION OF PRIOR ART

Breast cancer is the leading cause of cancer-related deaths for women in the world. It is the second most common cancer in females in India and the early detection and treatment improve prognosis and survival rate, motivating the need for finding out novel non-invasive methods for early diagnosis of this disease. Presently, biopsy is the only method which confirms the diagnosis and different grades of cancer. Being an invasive method, it is time consuming and often uncomfortable for the patient. Moreover, the negative biopsy rate is significantly high, especially in screen detected and non palpable cancers suggesting that better molecular diagnostic techniques are needed to replace or compliment current biopsy techniques. Tissue characterization by pathologists for ER, PR and HER 2/Neu status and axillary lymph node status are the most important prognostic factors and 90% of those patients without nodal involvement have no further breast cancers detected in their lifetime. Presently, there is no established non-invasive test for confirming the axillary node status. Axillary nodal status is of major importance from a therapeutic and prognostic point of view. Moreover, majority of patients end up doing chemotherapy due to lack of reliable markers. Chemotherapeutic drugs currently used are also not specific to breast cancer. Therefore it is imperative to find novel biomarkers for early and accurate diagnosis and prognosis in breast cancer sparing the majority of patients from undergoing an axillary dissection. Such molecular signatures can also lead to good prognosis and help develop novel targeted treatments. Moreover, such an approach can accurately identify subgroups of patients who will really benefit from cytotoxic chemotherapy with its debilitating side effects.

The diagnosis of breast tumor starts with the screening techniques to confirm whether a lump is present or not. The noninvasive examination techniques existing are mammography, ultrasound or MR imaging which determine the presence of any tumors and also detect tumor size, invasion etc. To further confirm the tumor diagnosis and grading, Fine Needle Aspiration and Cytology (FNAC), core biopsy and excisional biopsy is required. Additional testing may include genetic screening that test for the status of hormones like ER, PR, and genes like HER2/neu etc. Chemotherapy is currently used for all cases of Infiltrating duct carcinomas of breast.

Mammography and ultrasound may identify a potential area of concern. MRI imaging requires injection of a dye, the side effects of which are not yet proven. Fine Needle Aspiration Cytology (FNAC) is not always as reliable as surgical biopsies in producing a conclusive diagnosis. Immunohistochemical analysis of ER, PR, HER2/neu, BRCA and PTEN requires lot of time to arrive at any final conclusion of disease progression. The available diagnostic methods present in the market are not up to the expectation that one can diagnose the early stages of disease and therapeutic measures can be optimized to completely prevent and cure the tumor at right time. These above mentioned imaging tools are not sensitive methods to detect early molecular changes occurring in the cell during initiation of the cancer. Tissue embedding, sectioning, staining are all cumbersome procedures and time consuming. Moreover, staging could be determined only after getting the final histopathology report and extensive metastatic workup. No existing technologies are there for more accurate staging of the disease for identifying suitable patient sub groups to tailor systemic treatment. *There are no proper fast and accurate molecular diagnostic tool for pathologists till now for accurate staging and grading. As far as current chemotherapy regimens are concerned, no targeted therapy is currently used.

Biomarkers constitute the most important field in cancer diagnosis. Cancer biomarkers are especially useful for early detection or diagnosis of the disease. Biomarkers can be used to screen patients, for classifying the different stages or grades of cancers and to predict prognosis and resistance to therapy. A tumour marker can be produced by tumour itself or by the body as a result of the disease. These biomolecules are quite often produced in abnormally large numbers in the cancerous tissues and often secreted to body fluids like blood, serum, urine etc. To identify molecular changes setting-in much before the disease initiation and progression, development of molecular biomarkers is extremely important.

MicroRNAs are small RNAs of 22-25 nucleotides in length with a major role in gene regulation. Since they are highly conserved between the genomes of related species and show a characteristic evolutionary divergence, computational analysis of miRNAs would augment the experimental analysis to identify those which are involved in the regulation of common genes and pathways leading to the development of cancer. Recently, oncomiRs, special classes of non-coding microRNAs are found to be associated with a large number of cancers. Consequently impaired miRNA expression is implicated in various tumours. This class of novel non-coding RNAs or microRNAs is expected to eventually identify previously unappreciated tumour suppressors and oncogenes and also address many questions about the origin, development and progression of breast cancer. Many studies have shown a deregulation with respect to the expression of these small RNAs in many tumours. It is imperative to know the expression profile of these microRNAs which would help us to classify, and associate these miRNAs with different stages and grades of tumours so as to develop them as novel biomarkers of various cancers. Thus the expression profiles could be used for classification, prognosis and diagnosis of human malignancies

Present National and International Knowledge on the Utility of this Invention:

Disease in which S. Name of the objectives of investigation is No. Inventor investigation done Name of biomarkers 1. Eugene M. S. Detection of in Infectious disease Tissue specific miRNA (U.A.E.) vivo cell death 2 Talyor D. D. Diagnostic Diagnosis of Exosome associated miRNA (U.S.A.) marker cancer 3. Chen, Jian-Wei Post treatment Cancer hsa-miR-137, hsa-miR-372, (Taiwan) survival in hsa-miR-182*, hsa-miR-221, cancer and hsa-let-7a 4. Fischer T. J. Early stage Breast Cancer Biomarkers of the invention (U.S.A.) Breast cancer are proteins prognosis 5. Dmitrovsky, E. miRNA as Breast cancer MiRNAs are downregulated: (Hanover, NH, biomarker of hsa-miR-451, hsa-miR-143 US) human breast and hsa-miR-145. cancer 6. Croce C. M. Diagnosis, Breast cancer miRNAs are upregulated: (Columbus, OH, prognosis and hsa-miR-141, hsa-miR-200b, US) treatment of hsa-miR-200c, hsa-miR-221, breast cancer hsa-miR-222 and hsa-miR-21. miRNAs are Down regulated: hsa-miR-125b-1, has-miR125b- 2, has-miR-145, hsa -miR-21, has-miR-155, hsa -miR-10b

Other Examples of Similar Studies:

OncomiRs are a special class of non-coding microRNAs found to be associated with a large number of cancers. Consequently impaired miRNA expression is implicated in various tumours. Various in vitro and in vivo studies have implicated an active role of microRNAs in breast cancer. Many reports on microRNAs indicated their role in cell proliferation and apoptosis growth and migration (1 & 2) suggesting that deregulation of these microRNA could lead to proliferative diseases like cancer. Also studies have shown that microRNA cluster mapped to the hotspot areas of the genome that are prone for cancer mutations (3 & 4). Their expression patterns show a general trend of down regulation in human cancer samples (5) indicating that most of them function as tumour suppressors. Though many profiling studies have revealed a different signature of the cancer samples compared to normal tissues, very few studies have been conducted which elucidates the functional role of each of these microRNAs. In breast cancer, microRNA miR 206 was found to inhibit the function of estrogen receptor gene ESR1. Later, it was found to be targeted by a set of microRNAs like miR 18a, miR 18b, miR 193b and miR 302c (6 & 7). CyclinD1 which is over expressed in majority of the cancers was identified as a direct target of miR 17-5p (8). Under expression status of miR 125a & b in HER 2 positive tumours indicated their role as a tumour suppressor of this gene. Analysis of triple negative (ER, PR and HER2/Neu) breast cancer patients showed that expression levels of miR 210, miR 21, and miR 221 play a significant role in the primary breast cancer vs normal samples (15). Down regulation of miR 200 family members in highly metastatic tumours and their up regulation in mesenchymal cells which initiated mesenchymal to epithelial transition depicted its role in metastasis (10). Let 7, one of the founder members of microRNAs are usually under expressed in tumours. One of the studies revealed that their down regulation induced BTI-Cs (Breast—Tumour initiating Cells) for tumour initi ation, progression and metastasis and vice versa (11). MicroRNAs miR 21, miR 155 and miR 10b have been shown to play a role in tumour metastasis by targeting anti metastatic genes (12, 13,14). MiR-21 is over expressed in both male and female breast tumors compared with normal breast tissue and has been associated with advanced stage, lymph node positivity, and reduced survival time. Furthermore, existence of microRNAs either floating or in exosomes in the systemic circulation, has led to the possibility that such molecules may serve as biomarkers for early detection of cancers. Thus microRNA profiling is emerging as a powerful tool for diagnosis of breast cancer types, grades and stages. Although additional investigations are necessary to fully exploit the therapeutic use of miRNAs in breast cancer, there is increasing evidence that miRNAs have potential not only to facilitate the determination of diagnosis and prognosis and the prediction of response to treatment, but also to act as therapeutic targets and replacement therapies.

The drawback of these studies is that none of them was carried out in the specified stages of grades or subtypes of human breast cancer samples. Hence identifying the exact grade and stage of Breast Cancer is a boon for treatment of such kind of diseases.

OBJECTIVES OF THE INVENTION

The main objective of the present invention relates to biomarkers for diagnosis of different types, grades and stages of human breast cancer.

Another objective of the present invention relates to molecular biomarkers as indicators of cellular changes during the initiation and development of breast cancer.

Yet another objective of the present invention relates to a chip useful for detection and diagnosis of breast cancer.

Still another objective of the present invention is to provide a cheaper, accurate, robust and high throughput diagnostic kit for accurate diagnosis of human breast cancer.

SUMMARY OF THE INVENTION

Accordingly, the present invention relates to a panel of Biomarkers useful for screening and detection for the type, grade and stage of Breast cancer wherein the panel comprises of miRNA having sequence selected from the group consisting of Seq Id No. 1-107.

In an embodiment of the present invention a panel of Biomarkers useful for screening and detection for the type, grade and stage of Breast cancer wherein the panel comprises of miRNA having sequence selected from the group consisting of Seq Id No. 1-107

In an embodiment of the present invention the panel of Biomarkers wherein down regulation of microRNAs with Seq ID No. 1 to 12 and up regulation of Seq ID No. 13 to 15 detects ER+ve type of breast cancer.

In an embodiment of the present invention the panel of Biomarkers wherein down regulation of Seq. ID no. 16 & 17 and up regulation of Seq. ID no. 18 to 29 detects ER−Ve type of breast cancer.

In an embodiment of the present invention the panel of Biomarkers wherein down regulation of Seq. ID no. 30 to 36 and up regulation of Seq. ID no. 37 to 42 detects grade 2 breast cancer.

In an embodiment of the present invention the panel of Biomarkers wherein down regulation of Seq. ID no. 43 to 48 and up regulation of Seq. ID no. 49 & 50 detects grade 3 breast cancer.

In an embodiment of the present invention the panel of Biomarkers wherein down regulation of Seq. ID no. 51 to 55 detects stage I of grade 2 breast cancer.

In an embodiment of the present invention the panel of Biomarkers wherein down regulation of Seq. ID no. 56 to 73 detects stage II of grade 2 breast cancer.

In an embodiment of the present invention the panel of Biomarkers wherein down regulation of Seq. ID no. 74 & 75 and up regulation of Seq. ID no. 76 to 81 detects stage III of grade 2 breast cancer.

In an embodiment of the present invention the panel of Biomarkers wherein down regulation of Seq. ID no. 82 to 95 and up regulation of Seq. ID no. 96 & 97 detects stage I of grade 3 breast cancer.

In an embodiment of the present invention the panel of Biomarkers wherein down regulation of Seq. ID no. 98 to 100 and up regulation of Seq. ID no. 101 to 103 detects stage II of grade 3 breast cancer.

In an embodiment of the present invention the panel of Biomarkers wherein down regulation of Seq. ID no. 104 & 105 and upregulation of Seq. ID no. 106 & 107 detects stage III of grade 3 breast cancer.

In an embodiment of the present invention the panel of Biomarkers wherein the antisense sequences of the upregulated & downregulated microRNAs is of therapeutic use.

In yet another embodiment of the present invention an in vitro non-invasive method using the panel of biomarkers as claimed in claim 1 for detecting the type, grade and stage of breast cancer in a human subject.

In yet another embodiment of the present invention a panel of miRNA in the form of a DNA/RNA chip.

In yet another embodiment of the present invention a kit for detecting type, grade and stage of breast cancer wherein the kit consisting of:

Suitable reagents capable of detecting singly or a combination of the miRNA; Instruction manual for using the kit.

In yet another embodiment of the present invention use of the biomarkers and their antisense sequence for screening, diagnosis, prognosis and for preparing biological drugs for Breast Cancer.

In yet another embodiment of the present invention use of the biomarkers for detection of type, grades and stages of Breast Cancer.

In yet another embodiment of the present invention use of the biomarkers in diagnosis and prognosis of Breast cancer.

In yet another embodiment of the present invention use of the kit for detection of type, grades and stages of Breast Cancer.

BRIEF DESCRIPTION OF TABLES AND FIGURES

Table 1: The sequence IDs 1 to 15 lists the microRNAs which are significantly up/down regulated in ER+ve human breast cancer samples. The microRNA names along with their sequences and accession IDs are also described here. The exact fold change with which each microRNA is down regulated (indicated by +ve sign) and those which are up regulated 9 indicated by −Ve sign) are also given.

Table 2: The sequence IDs 16 to 29 lists the microRNAs which are significantly up/down regulated in ER−ve human breast cancer samples. The microRNA names along with their sequences and accession IDs are also described here. The exact fold change with which each microRNA is down regulated (indicated by +ve sign) and those which are upregulated 9 indicated by −Ve sign) are also given.

Table 3: The sequence IDs 30 to 42 lists the microRNAs which are significantly up/down regulated in grade 2 human breast cancer samples. The microRNA names along with their sequences and accession IDs are also described here. The exact fold change with which each microRNA is down regulated (indicated by +ve sign) and those which are upregulated 9 indicated by −Ve sign) are also given.

Table 4: The sequence IDs 43 to 50 lists the microRNAs which are significantly up/down regulated in grade 3 human breast cancer samples. The microRNA names along with their sequences and accession IDs are also described here. The exact fold change with which each microRNA is down regulated (indicated by +ve sign) and those which are upregulated 9 indicated by −Ve sign) are also given.

Table 5: The sequence IDs 51 to 55 lists the microRNAs which are significantly up/down regulated in stage I of grade 2 human breast cancer samples. The microRNA names along with their sequences and accession IDs are also described here. The exact fold change with which each microRNA is down regulated (indicated by +ve sign) and those which are upregulated 9 indicated by −Ve sign) are also given.

Table 6: The sequence IDs 56 to 73 lists the microRNAs which are significantly up/down regulated in stage II of grade 2 human breast cancer samples. The microRNA names along with their sequences and accession IDs are also described here. The exact fold change with which each microRNA is down regulated (indicated by +ve sign) and those which are upregulated 9 indicated by −Ve sign) are also given.

Table 7: The sequence IDs 74 to 81 lists the microRNAs which are significantly up/down regulated in stage III of grade 2 human breast cancer samples. The microRNA names along with their sequences and accession IDs are also described here. The exact fold change with which each microRNA is down regulated (indicated by +ve sign) and those which are upregulated 9 indicated by −Ve sign) are also given.

Table 8: The sequence IDs 82 to 97 lists the microRNAs which are significantly up/down regulated in stage I of grade 3 human breast cancer samples. The microRNA names along with their sequences and accession IDs are also described here. The exact fold change with which each microRNA is down regulated (indicated by +ve sign) and those which are upregulated 9 indicated by −Ve sign) are also given.

Table 9: The sequence IDs 98 to 103 lists the microRNAs which are significantly up/down regulated in stage II of grade 3 human breast cancer samples. The microRNA names along with their sequences and accession IDs are also described here. The exact fold change with which each microRNA is down regulated (indicated by +ve sign) and those which are upregulated 9 indicated by −Ve sign) are also given.

Table 10: The sequence IDs 104 to 107 lists the microRNAs which are significantly up/down regulated in stage III of grade 3 human breast cancer samples. The microRNA names along with their sequences and accession IDs are also described here. The exact fold change with which each microRNA is down regulated (indicated by +ve sign) and those which are upregulated 9 indicated by −Ve sign) are also given.

Table 11: miRNAs validated and reconfirmed by Individual Taqman assays in different grades and stages of Breast cancer spotted on a biochip.

FIG. 1: The sequence IDs 1 to 15 lists the microRNAs which are significantly up/down regulated in ER+ve and non-significant in ER−ve human breast cancer samples. The heat map represents the up and down regulation with respective p values.

FIG. 2: The sequence IDs 16 to 29 lists the microRNAs which are significantly up/down regulated in ER−ve and non-significant in ER+ve human breast cancer samples. The heat map represents the up and down regulation with respective p values.

FIG. 3: The sequence IDs 30 to 42 lists the microRNAs which are significantly up/down regulated in grade 2 and non-significant in grade 3 human breast cancer samples. The heat map represents the up and down regulation with respective p values.

FIG. 4: The sequence IDs 43 to 50 lists the microRNAs which are significantly up/down regulated in grade 3 and non-significant in grade 2 human breast cancer samples. The heat map represents the up and down regulation with respective p values.

FIG. 5: The sequence IDs 51 to 55 lists the microRNAs which are significantly up/down regulated in Stage I of grade 2 and non-significant in stage II and III of grade 2 human breast cancer samples. The heat map represents the up and down regulation with respective p values.

FIG. 6: The sequence IDs 56 to 73 lists the microRNAs which are significantly up/down regulated in Stage II of grade 2 and non-significant in stage I and III of grade 2 human breast cancer samples. The heat map represents the up and down regulation with respective p values.

FIG. 7: The sequence IDs 74 to 81 lists the microRNAs which are significantly up/down regulated in Stage III of grade 2 and non-significant in stage I and II of grade 2 human breast cancer samples. The heat map represents the up and down regulation with respective p values.

FIG. 8: The sequence IDs 82 to 97 lists the microRNAs which are significantly up/down regulated in Stage I of grade 3 and non-significant in stage II and III of grade 3 human breast cancer samples. The heat map represents the up and down regulation with respective p values.

FIG. 9:The sequence IDs 98 to 103 lists the microRNAs which are significantly up/down regulated in Stage II of grade 3 and non-significant in stage I and III of grade 3 human breast cancer samples. The heat map represents the up and down regulation with respective p values.

FIG. 10: The sequence IDs 104 to 107 lists the microRNAs which are significantly up/down regulated in Stage III of grade 3 and non-significant in stage I and II of grade 3 human breast cancer samples. The heat map represents the up and down regulation with respective p values.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

Breast cancer is a complex heterogeneous genetic disease, involving a variety of changes in gene expression and structure. MicroRNAs are recently discovered tiny RNA molecules which play an important role in the gene regulation. They are found to have an altered expression in majority of cancers and are termed as oncomiRs. Recently, advances in molecular profiling has shed new light on the etiology of the disease and also acclaimed great potential for the development of novel biomarkers for diagnosis, prognosis and therapeutic targets. This attracts the scientific domain for extensive investigation to further elucidate their precise role as novel biomarkers in malignancy.

MicroRNAs are tiny biological molecules that play a regulatory role in biological processes and cellular functions. Therefore these molecules could be used as indicators of changes in the cells, when they transform from normal to diseased condition. This invention specifically relates to the identification of changes in these small RNA regulations that play an important role in the development of breast cancer. Thus creating an expression signature of these microRNAs involved in cancer (oncomiRs) at particular stages of development or disease progression qualifies them as ideal biomarkers. Thus we have identified changes in the expression pattern of small RNAs called oncomiRs from breast cancer patients at different grades and stages of development of cancer. The expression profile of these miRNAs formed a classic signature, as breast cancer progressed from stage I to stage III, in both grades. These differentially up and down regulated microRNAs are significant in one type, stage and grade of cancer and not in the other. Therefore we have classified them into type, grade and stage specific biomarkers which could be useful tools in the diagnosis and prognosis of breast cancer.

The role of miRNAs as gene regulators distinguishes them as novel biomarkers for diagnosis and prognosis in various cancers. MiRNAs possess unique features that classify them as ideal tumor markers include their tissue specificity, stability, ease of detection and association with the disease status. Thus miRNAs have vast possibility in diagnosis, prognosis and treatment of diseases especially malignancies like breast cancer, where still no reliable tumor markers for particular stages and grades are available at present. Potentially these molecular biomarkers can be used to accurately identify subgroups of patients who will really benefit from cytotoxic chemotherapy with its debilitating side effects. Thus this proves to be an additional, accurate, quick and high throughput molecular diagnostic tool for pathologists especially when patient number is high.

Molecular changes starts in a cell much before morphological changes occur. Our invention has made it possible to detect these early changes, which lead to the initiation and progression of breast cancer. Moreover, large number of samples could be tested at one go in less than 2 hours time, making this a high through put assay and cost effective assay. Finding the deregulated targets of microRNAs has great potential in targeted therapy.

The novelty of these miRNAs is that they detect the early molecular changes in the cell. Thus they are ideal and potential biomarkers for detecting different grades and stages of breast cancer. A few to hundred samples can be checked within a span of 2 to 3 hrs and hence this becomes an easy, fast and high throughput technology.

Though microRNAs are present in the cells and altered signatures are detected and reported in cancer samples, the identification of the different subtype, grade and stage specific microRNAs along with its fold regulation demarcated them as ideal biomarkers for breast cancer diagnosis, prognosis and targeted therapy.

Specific microRNAs are identified by LNA microarray and is verified with Taqman Low Density arrays. The fold regulation of each microRNA was also found by TLDA analysis. Additional validation of these microRNAs were carried out using Taqman individual assays in individual cancer samples and identified for making this as a diagnostic chip (Table 11).

Workflow of miRNA Profiling

-   -   RNA was isolated from Breast cancer tissues along with adjacent         normals using mirVana™ miRNA Isolation Kit.         -   1-350 ng total RNA was used for Quantitative Reverse             transcriptase reaction.     -   Megaplex Reverse Transcription r×n (40 cycles) is done using         Megaplex RT Primers, (TaqMan® MicroRNA Reverse Transcription         Kit) -dNTPs with dTTP, Multiscribe™ reverse Transcriptase, 10×         RT Buffer and RNase Inhibitor.         -   The reverse transcription (RT) reaction was done in a final             volume of 7.5 μL which contains: 3 μL total RNA and 4.5 μL             of RT reaction mix. Thermocycling condition was set as             default and Ramp speed or mode: 9700 using Std or Max ramp.     -   Preamplification (12 cycles) were done using Megaplex PreAmp         Primers, TaqMan® PreAmp Master Mix, 2×.         -   In this step, pre amplified specific cDNA targets were             subjected to increase, the quantity of desired cDNA for             miRNA expression analysis using TaqMan® MicroRNA Arrays. The             preamplification reaction was performed in a final volume of             25 μL where: 2.5 μL RT product and 22.5 μL PreAmp reaction             mix was present.     -   Real-Time PCR Reaction were done using TaqMan® Universal PCR         Master Mix, No AmpErase UNG 2×, TaqMan® MicroRNA Array.         -   Here the DNA polymerase from the TaqMan® Universal PCR             Master Mix amplifies the target cDNA using sequence-specific             primers and probe on the TaqMan® MicroRNA Array. The             presence of the target is detected in real time through             cleavage of the TaqMan probe by the polymerase 5′-3′             exonuclease activity.

1. Megaplex RT product and TaqMan Universal PCR Master mix in total volume of 900 μL was mixed.

2. Dispensed 100 μL of the PCR reaction mix into each port of the TaqMan MicroRNA Array centrifuged and then sealed the array.

3. Imported the SDS setup file (SDS.txt) SDS software v2.2 and set for Relative Quantification (ΔΔCt) in 384 well TaqMan Low Density Array.

4. Loaded and ran the array using the 384 well TaqMan Low Density Array default thermal-cycling conditions.

Data Analysis

1. To analyze the results the SDS files were transferred into an RQ study format.

2. Amplification plots, baseline and threshold values were adjusted

3. Threshold cycles (CTs) were compared and analyzed using arithmetic formulas that determines the change in expression of a target gene in an experimental sample relative to the same target in a reference sample. This method was used for high-throughput measurements of relative gene expression.

4. Statminer software was used for fold expression analysis of miRNAs and classified as detector not amplified, significant and nonsignificant based on their p values. Highly significant (≥0.05) miRNAs were selected for study and biomarker identification in the respective types of breast cancer.

EXAMPLES

The following examples are given by way of illustration of the present invention and therefore should not be construed to limit the scope of the present invention.

Example 1 Total RNA Isolation and Quality Control

Tissue samples (100 mg) were homogenized using automated tissue homogenizer and total RNAs were isolated using miRvana kit (Ambion) from all the samples. The quantity of these RNAs was checked using nanodrop and spectophotometer and quality using RIN (RNA Integrity Number) values in Agilent Bioanalyzer. These RNAs were used in all downstream experiments. The reverse transcription reaction was performed using the TaqMan MicroRNA Reverse Transcription Kit followed by Polymerase Chain Reaction. Real-time Polymerase Chain Reaction was performed using an Applied Biosystems 7900-Taqman Low Density Array Real-Time Polymerase Chain Reaction System. Each TaqMan Assay was run in quadruplicate. All the samples displayed good RIN value, linearity (R 2>0.96), good abundance (average CT range 22-28) and NTC (Non-Template Control) CT >38.

Example 2 Taqman Low Density Arrays (TLDA)

MicroRNA profiling was done using TaqManMicroRNA Arrays, which contains megaplex Primer Pools covering Sanger miRBase version 10. Megaplex Reverse Transcription Primers are novel stem-looped RT primer pools that streamline the profiling of hundreds of miRNA targets in a single experiment and reduce the number of Reverse Transcription reactions and the amount of total RNA required for generating a comprehensive miRNA expression profile. A pre amplification step of cDNA with preamp megaplex pool primers was done to significantly enhance the ability to detect slowly expressed miRNAs.

Example 3 Real Time Amplification of miRNA Pool By Loading in TLDA Plates

The TaqMan human MicroRNA arrays consists of 2 plates pool A and pool B. ‘A’ Array Sanger's V 10.0 contains 667 human taqman microRNA Assays. Three TaqMan MicroRNA Assay. Endogenous controls are included for data normalization and one TaqMan MicroRNA Assay, not related to human is also included as a negative control. The set enables accurate quantitation of 667 human microRNAs.

Results of Taqman Low Density Arrays Analysis

The statistical analysis was performed using statminer software. This contains a filtering procedure for outlier removal; various normalization methods based on single or multiple genes and provides relative quantification analysis of gene expression through a combination of statistical analysis and interactive visualization.

The CT (threshold cycle) values for each well were adjusted and included/excluded for analysis based on the following analysis settings:

if a CT>=Max CT, it was adjusted to Max CT. and calculated the deviation G in units of the standard deviation (SD): G=(max CT−mean CT)/SD. If the following test is true, and (max CT−mean CT)>=0.25, then the replicate with max CT is removed as outlier. Arithmetic Mean uses the arithmetic mean of CT values of the selected controls as the normalization factor (NF), while Geometric Mean uses their geometric mean as the NF. Pearson's product moment correlation coefficient (r) was calculated for CT or ΔCT values of sample pairs, and plotted on the Signal Correlation Plot and Scatter Plot respectively. T-test was performed to calculate p-value. Standard deviation (SD) was calculated for CT values of the technical replicates, and is used to calculate the RQ (fold change).

Based on this analysis, different sets of microRNAs were selected which pertains to different subtypes (ER+ve, ER−ve), grades and stages (Table 1, 2 and 3).

The list of highly significant miRNAs in breast cancer with different types, grades and stages used as novel biomarker for diagnosis and prognosis of breast cancer patient is provided below:

TABLE 1  MicroRNA significantly up/down regulated in ER + ve Sequence MicroRNAs Sequence Accession Id Fold ID hsa-miR-623 AUCCCUUGCAGGGGCUGUUGGGU MIMAT0003292 −40.85544 1 hsa-miR-302d UAAGUGCUUCCAUGUUUGAGUGU MIMAT0000718 −34.48034 2 hsa-miR-562 AAAGUAGCUGUACCAUUUGC MIMAT0003226 −31.96053 3 hsa-miR-224 CAAGUCACUAGUGGUUCCGUU MIMAT0000281 −17.45599 4 hsa-miR-452 AACUGUUUGCAGAGGAAACUGA MIMAT0001635 −17.33216 5 hsa-miR-522 AAAAUGGUUCCCUUUAGAGUGU MIMAT0002868 −15.13811 6 hsa-miR-124 UAAGGCACGCGGUGAAUGCC MIMAT0000422 −12.27811 7 hsa-miR-516a-5p UUCUCGAGGAAAGAAGCACUUUC MIMAT0004770 −11.79572 8 hsa-miR-521 AACGCACUUCCCUUUAGAGUGU MIMAT0002854 −10.90129 9 hsa-miR-627 GUGAGUCUCUAAGAAAAGAGGA MIMAT0003296 −4.234165 10 hsa-miR-650 AGGAGGCAGCGCUCUCAGGAC MIMAT0003320 −3.254617 11 hsa-miR-205 UCCUUCAUUCCACCGGAGUCUG MIMAT0000266 −3.148418 12 hsa-miR-605 UAAAUCCCAUGGUGCCUUCUCCU MIMAT0003273 13.311642 13 hsa-miR-375 UUUGUUCGUUCGGCUCGCGUGA MIMAT0000728 13.609262 14 hsa-miR-190b UGAUAUGUUUGAUAUUGGGUU MIMAT0004929 40.579717 15 pvalue 0.01-2.40E−14

TABLE 2  MicroRNA significantly up/down regulated in ER - ve Sequence MicroRNAs Sequence Accession Id Fold ID hsa-miR-887 GUGAACGGGCGCCAUCCCGAGG MIMAT0004951 −10.90658 16 hsa-miR-126* CAUUAUUACUUUUGGUACGCG MIMAT0000444 −3.717792 17 hsa-miR-188-5p CAUCCCUUGCAUGGUGGAGGG MIMAT0000457 2.600684 18 hsa-miR-210 CUGUGCGUGUGACAGCGGCUGA MIMAT0000267 3.6747714 19 hsa-miR-20a UAAAGUGCUUAUAGUGCAGGUAG MIMAT0000075 3.8147056 20 hsa-miR-31 AGGCAAGAUGCUGGCAUAGCU MIMAT0000089 4.1211402 21 hsa-miR-187 UCGUGUCUUGUGUUGCAGCCGG MIMAT0000262 4.6737121 22 hsa-miR-301b CAGUGCAAUGAUAUUGUCAAAGC MIMAT0004958 5.6936425 23 hsa-miR-142-3p UGUAGUGUUUCCUACUUUAUGGA MIMAT0000434 5.9133475 24 hsa-miR-18a UAAGGUGCAUCUAGUGCAGAUAG MIMAT0000072 6.9884545 25 hsa-miR-137 UUAUUGCUUAAGAAUACGCGUAG MIMAT0000429 7.8730989 26 hsa-miR-9 UCUUUGGUUAUCUAGCUGUAUGA MIMAT0000441 8.1181347 27 hsa-miR-135b* AUGUAGGGCUAAAAGCCAUGGG MIMAT0004698 8.6834163 28 hsa-miR-934 UGUCUACUACUGGAGACACUGG MIMAT0004977 15.642491 29 pvalue 0.01-0.00098

TABLE 3  MicroRNA significantly up/down regulated in Grade 2 Sequence MicroRNAs Sequence Accession Id Fold ID hsa-miR-143* GGUGCAGUGCUGCAUCUCUGGU MIMAT0004599 −78.86936 30 hsa-miR-361-3p UCCCCCAGGUGUGAUUCUGAUUU MIMAT0004682 −20.75945 31 hsa-miR-129-3p AAGCCCUUACCCCAAAAAGCAU MIMAT0004605 −10.96402 32 hsa-miR-561 CAAAGUUUAAGAUCCUUGAAGU MIMAT0003225 −4.984571 33 hsa-miR-548b-5p AAAAGUAAUUGUGGUUUUGGCC MIMAT0004798 −4.389338 34 hsa-miR-627 GUGAGUCUCUAAGAAAAGAGGA MIMAT0003296 −4.370396 35 hsa-miR-92a-1* AGGUUGGGAUCGGUUGCAAUGCU MIMAT0004507 −1.840965 36 hsa-miR-93* ACUGCUGAGCUAGCACUUCCCG MIMAT0004509 1.4612617 37 hsa-miR-571 UGAGUUGGCCAUCUGAGUGAG MIMAT0003236 2.2381639 38 hsa-miR-7-1* CAACAAAUCACAGUCUGCCAUA MIMAT0004553 2.4298469 39 hsa-miR-26a-2* CCUAUUCUUGAUUACUUGUUUC MIMAT0004681 2.9292837 40 hsa-miR-449b AGGCAGUGUAUUGUUAGCUGGC MIMAT0003327 10.183938 41 hsa-miR-449a UGGCAGUGUAUUGUUAGCUGGU MIMAT0001541 16.080831 42 pvalue 0.01-9.09E−06

TABLE 4  MicroRNA significantly up/down regulated in Grade 3 Sequence MicroRNAs Sequence Accession Id Fold ID hsa-miR-195* CCAAUAUUGGCUGUGCUGCUCC MIMAT0004615 −230.2186 43 hsa-miR-567 AGUAUGUUCUUCCAGGACAGAAC MIMAT0003231 −11.57537 44 hsa-miR-29c* UGACCGAUUUCUCCUGGUGUUC MIMAT0004673 −4.963266 45 hsa-miR-30e* CUUUCAGUCGGAUGUUUACAGC MIMAT0000693 −3.293634 46 hsa-miR-30a* CUUUCAGUCGGAUGUUUGCAGC MIMAT0000088 −3.10055 47 hsa-miR-29b-2* CUGGUUUCACAUGGUGGCUUAG MIMAT0004515 −2.687606 48 hsa-miR-135b UAUGGCUUUUCAUUCCUAUGUGA MIMAT0000758 6.416591 49 hsa-miR-767-5p UGCACCAUGGUUGUCUGAGCAUG MIMAT0003882 101.53822 50 pvalue 0.01-8.5E−07

TABLE 5  MicroRNA significantly up/down regulated in Grade 2 Stage I Sequence MicroRNAs Sequence Accession Id Fold ID hsa-miR-874 CUGCCCUGGCCCGAGGGACCGA MIMAT0004911 −86.318 51 hsa-miR-487a AAUCAUACAGGGACAUCCAGUU MIMAT0002178 −41.49081 52 hsa-miR-655 AUAAUACAUGGUUAACCUCUUU MIMAT0003331 −13.23111 53 hsa-miR-30d* CUUUCAGUCAGAUGUUUGCUGC MIMAT0004551 −6.504431 54 hsa-miR-136 ACUCCAUUUGUUUUGAUGAUGGA MIMAT0000448 −6.321441 55 pvalue 0.0067-0.003

TABLE 6  MicroRNA significantly up/down regulated in Grade 2Stage II Sequence MicroRNAs Sequence Accession Id Fold ID hsa-miR-509-5p UACUGCAGACAGUGGCAAUCA MIMAT0004779 −34.51461 56 hsa-miR-365 UAAUGCCCCUAAAAAUCCUUAU MIMAT0000710 −8.811865 57 hsa-miR-92a UAUUGCACUUGUCCCGGCCUGU MIMAT0000092 −8.117638 58 hsa-miR-130a CAGUGCAAUGUUAAAAGGGCAU MIMAT0000425 −8.054065 59 hsa-miR-532-3p CAUGCCUUGAGUGUAGGACCGU MIMAT0002888 −6.646912 60 hsa-miR-30b UGUAAACAUCCUACACUCAGCU MIMAT0000420 −6.620686 61 hsa-miR-140-5p CAGUGGUUUUACCCUAUGGUAG MIMAT0000431 −6.46631 62 hsa-miR-362-5p AAUCCUUGGAACCUAGGUGUGAGU MIMAT0000705 −6.386795 63 hsa-miR-221 AGCUACAUUGUCUGCUGGGUUUC MIMAT0000278 −6.37909 64 hsa-let-7e UGAGGUAGGAGGUUGUAUAGUU MIMAT0000066 −6.094803 65 hsa-miR-324-5p CGCAUCCCCUAGGGCAUUGGUGU MIMAT0000761 −6.072664 66 hsa-let-7a UGAGGUAGUAGGUUGUAUAGUU MIMAT0000062 −5.936147 67 hsa-let-7d AGAGGUAGUAGGUUGCAUAGUU MIMAT0000065 −5.833018 68 hsa-miR-25 CAUUGCACUUGUCUCGGUCUGA MIMAT0000081 −5.692002 69 hsa-miR-20b CAAAGUGCUCAUAGUGCAGGUAG MIMAT0001413 −5.261795 70 hsa-miR-491-5p AGUGGGGAACCCUUCCAUGAGG MIMAT0002807 −4.981938 71 hsa-miR-99b CACCCGUAGAACCGACCUUGCG MIMAT0000689 −4.539054 72 hsa-miR-345 GCUGACUCCU   AGUCCAGGGC   UC MIMAT0000825 −3.639054 73 pvalue 0.01-0.00055

TABLE 7  MicroRNA significantly up/down regulated in Grade 2 Stage III Sequence MicroRNAs Sequence Accession Id Fold ID hsa-miR-661 UGCCUGGGUCUCUGGCCUGCGCGU MIMAT0003324 −72.56704 74 hsa-miR-376a* GUAGAUUCUCCUUCUAUGAGUA MIMAT0003386 −4.924847 75 hsa-miR-625* GACUAUAGAACUUUCCCCCUCA MIMAT0004808 1.6739604 76 hsa-miR-766 ACUCCAGCCCCACAGCCUCAGC MIMAT0003888 1.7897822 77 hsa-miR-200c UAAUACUGCCGGGUAAUGAUGGA MIMAT0000617 5.6291585 78 hsa-miR-598 UACGUCAUCGUUGUCAUCGUCA MIMAT0003266 6.1447238 79 hsa-miR-135a UAUGGCUUUUUAUUCCUAUGUGA MIMAT0000428 9.0314152 80 hsa-miR-184 UGGACGGAGAACUGAUAAGGGU MIMAT0000454 22.902401 81 pvalue 0.01-0.00037

TABLE 8  MicroRNA significantly up/down regulated in Grade 3 Stage I Sequence MicroRNAs Sequence Accession Id Fold ID hsa-miR-654-5p UGGUGGGCCGCAGAACAUGUGC MIMAT0003330 −61.18959 82 hsa-miR-154 UAGGUUAUCCGUGUUGCCUUCG MIMAT0000452 −55.31606 83 hsa-miR-499-5p UUAAGACUUGCAGUGAUGUUU MIMAT0002870 −42.64146 84 hsa-miR-299-5p UGGUUUACCGUCCCACAUACAU MIMAT0002890 −37.59992 85 hsa-miR-431 UGUCUUGCAGGCCGUCAUGCA MIMAT0001625 −16.19831 86 hsa-miR-381 UAUACAAGGGCAAGCUCUCUGU MIMAT0000736 −13.54713 87 hsa-miR-337-5p GAACGGCUUCAUACAGGAGUU MIMAT0004695 −13.22481 88 hsa-miR-369-5p AGAUCGACCGUGUUAUAUUCGC MIMAT0001621 −10.50421 89 hsa-miR-154* AAUCAUACACGGUUGACCUAUU MIMAT0000453 −9.909352 90 hsa-miR-615-5p GGGGGUCCCCGGUGCUCGGAUC MIMAT0004804 −8.392234 91 hsa-miR-542-5p UCGGGGAUCAUCAUGUCACGAGA MIMAT0003340 −7.18576 92 hsa-miR-539 GGAGAAAUUAUCCUUGGUGUGU MIMAT0003163 −4.765181 93 hsa-miR-379 UGGUAGACUAUGGAACGUAGG MIMAT0000733 −3.923594 94 hsa-miR-376a AUCAUAGAGGAAAAUCCACGU MIMAT0000729 −3.799978 95 hsa-miR-19a* AGUUUUGCAUAGUUGCACUACA MIMAT0004490 8.2999188 96 hsa-miR-586 UAUGCAUUGUAUUUUUAGGUCC MIMAT0003252 9.2991122 97

TABLE 9  MicroRNA significantly up/down regulated in Grade 3 Stage II Sequence MicroRNAs Sequence Accession Id Fold ID hsa-miR-760 CGGCUCUGGGUCUGUGGGGA MIMAT0004957 −5.391269 98 hsa-let-7e* CUAUACGGCCUCCUAGCUUUCC MIMAT0004485 −1.33367 99 hsa-miR-30d UGUAAACAUCCCCGACUGGAAG MIMAT0000245 −1.683826 100 hsa-miR-27a* AGGGCUUAGCUGCUUGUGAGCA MIMAT0004501 1.3726469 101 hsa-miR-941 CACCCGGCUGUGUGCACAUGUGC MIMAT0004984 1.4397387 102 hsa-miR-493* UUGUACAUGGUAGGCUUUCAUU MIMAT0002813 1.9122828 103 pvalue 0.0023-0.00085

TABLE 10  MicroRNA significantly up/down regulated in Grade 3 Stage III Sequence MicroRNAs Sequence Accession Id Fold ID hsa-miR-584 UUAUGGUUUGCCUGGGACUGAG MIMAT0003249 −13.69725 104 hsa-miR-193b* CGGGGUUUUGAGGGCGAGAUGA MIMAT0004767 −8.708156 105 hsa-miR-200c* CGUCUUACCCAGCAGUGUUUGG MIMAT0004657 6.7748051 106 hsa-miR-147b GUGUGCGGAAAUGCUUCUGCUA MIMAT0004928 12.909898 107 pvalue 0.008-0.001

Example 4 LNA Microarray

The differentially expressed microRNAs identified by Taqman Low Density Arrays were further confirmed with LNA (Locked Nucleic Acid) Array. The RNA isolated from the same cancer samples were hybridized against 2002 microRNAs consisting of 904 human, 388 rat and 710 mouse microRNAs. The normals were labeled with Hy5 dye and samples were labeled with Hy3 and also reversely hybridised and taken the mean intensities for calculation.

The normalized median signal intensities for the Hy3 (sample) and Hy5 (common reference) indicate the relative expression level of each microRNA in the samples and in the common reference. If the Hy3 value is higher than the Hy5 value there is a higher expression in the sample than in the common reference and if the Hy3 value is lower than that of Hy5 value there is a lower expression in the samples compared to the common reference. Then we take the ratios between the Hy3 and Hy5 signal and the log 2 to that ratio. A positive number indicates a higher expression in the sample (Hy3) compared to the common reference and vice versa. The NA means that the microRNA is not expressed based on certain cut-off criteria. One criterion is the signal intensity of the Hy3 and Hy5 channel. If both Hy3 and Hy5 signals are below 1.5 times the median of all capture probes on the array we say that it is background and below our cut-off. This cut-off is set to avoid too many false positives.

Call rate is the number of expressed microRNAs compared to the total number of microRNAs analyzed (the % of identified microRNAs). This call rate is expected to be between 20 and 50% for human samples and it is clear that we have a very nice and high call rate in our samples. That human samples have call rates between 20 and 50% has been documented in the literature, both based on deep sequencing, array and PCR profiling. A call rate much higher than 50% indicates a high risk of having false positives in the data set Therefore we used the 1.5× median of all capture probes as a cut off.

The microRNAs have been analyzed based on the samples groups. A two-tailed statistical t-test has been performed between the samples groups grade 2 and grade 3. The heat map has been made based on a cut-off of P<10⁻³. “Expression matrix (analysis)” looked like typical breast cancer microRNAs. A very long list of breast cancer miRNAs from literature, web databases are all present in our samples. Just to mention some examples, miR-21, miR-155, miR-148a, miR 210 and miR-29b. These typical breast cancer signatures clearly classify our samples as breast cancer samples.

The identification of miRNAs in particular stages or grades shows its behavior which is highly correlated with the expression of translational regulators or targets that are involved in tumor progression. The miRNAs which are down regulated at stage I, gets up or down regulated successively at stage II and stage III with in a grade. This classical pattern of miRNA expression indicates their importance in controlling the progressive growth of breast cancer.

The findings of these significant novel miRNAs in specific stages and grades will enable us to design individual assays for their validation in vitro and invivo. These validated miRNAs may give new insight for the diagnosis and treatment of tumor, progressing at specific stages or grades.

Furthermore, these differentially up/down regulated miRNAs in various stages of breast cancer identified by TLDA technique, have also been confirmed by LNA microarry technology. This also reconfirms the trend of expression pattern in aforesaid stages and grades of breast cancer. These finding indicated that the expression of common miRNAs in both the techniques have some defined role in the tumor progression. Among these common miRNAs, few highly up/down ones are selected for their individual assay validation is been done to prove these candidate miRNAs as novel biomarker for particular grade/stage of breast cancer.

Example 5 Validation of Highly Up/Down Regulated MicroRNAs By Q-per

Six of the highly up and down regulated category of microRNAs which are common among the TLDA microarray and LNA microarray were selected for its further validation among the rest of individual samples in grade 2 and grade 3 by q-per analysis. (Table 11)

Advantages

These novel biomarkers could be developed as a diagnostic kit for early and accurate diagnosis of human breast cancer. They are direct indicators of cellular changes during the initiation and development of breast cancer. These biomarkers complement the pathologists for the accurate grading and staging of breast cancer. These biomarkers provided a utility angle to the already existing biological molecules called microRNAs which play a major role in gene regulation. These biomarkers could provide a fast, cheaper, accurate, robust and high throughput diagnostic kit for accurate diagnosis of human breast cancer.

TABLE 11 miRNAs validated and reconfirmed by Individual Taqman assays in different grades and stages of Breast cancer spotted on a biochip. Fold Up/Down Individual Taqman assay Taqman Low Density Array ER − ve miR-9

11.4

7.8 miR-135b*

10.4

8.1 miR-137

181.4

8.6 ER + ve miR-605

6.6

13.3 miR-375

48.4

13.6 miR-190b

135.1

40.5 GR 2 miR-7-1*

5.6

2.9 miR-449a

19.4

16.1 miR-449b

23.0

10.8 GR 3 miR-135b

70.3

6.4 miR-767-5p

22.8

101.5 GR 2Stg I miR-487a

−2.4

−41.4 miR-655

−3.5

−13.2 miR-874

−2.0

−86.3 GR 2Stg II let-7d

−7.6

−5.8 miR-365

−11.4

−8.8 GR 2Stg III miR-135a

6.6

9.1 miR-200c

7.2

5.6 miR-184

26.2

22.9 GR 3Stg I miR-19a*

2.8

8.2 miR-586

4.2

9.2 miR-654-5p

−2.1

−61.1 GR 3Stg II miR-30d

−1.6

−1.6 miR-493*

4.3

1.9 miR-941

3.5

1.4 GR 3Stg III miR-193b*

−2.5

−8.7 miR-584

−3.7

−13.7 miR-200c*

1.7

6.7 miR-147b

17.8

12.9

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We claim:
 1. A microarray consisting of a panel of probes for miRNAs affixed to the microarray, which panel is useful for screening and detection for the type, grade and stage of breast cancer, wherein the miRNAs consist of SEQ ID Nos. 1-107, or a subset thereof consisting of SEQ ID Nos. 30-42 or SEQ ID Nos. 51-55.
 2. The panel as claimed in claim 1, wherein SEQ ID Nos. 30-42 detect grade 2 breast cancer.
 3. The panel as claimed in claim 1, wherein SEQ ID Nos. 51-55 detect stage I of grade 2 breast cancer.
 4. A kit for detecting type, grade and stage of breast cancer wherein the kit consists of: I. a microarray as claimed in claim 1, II. suitable reagents capable of detecting singly or a combination of the miRNA; and III. an instruction manual for using the kit.
 5. An in vitro non-invasive method using the panel as claimed in claim 1 for detecting the type, grade and stage of breast cancer in a human subject, comprising: hybridizing a breast tissue sample comprising RNA or cDNA of the RNA from a human with the microarray of claim 1, wherein the sample is suspected of having breast cancer cells; and detecting the type, grade and/or stage of breast cancer based on the relative level and profile of hybridization to the panel of probes in the microarray, wherein the type detected is estrogen receptor positive or estrogen receptor negative, wherein the grade detected is grade 2 or grade 3, and wherein the stage detected is stage I, stage II or stage III.
 6. The method as claimed in claim 5, wherein SEQ ID Nos. 30-42detect grade 2 breast cancer.
 7. The method as claimed in claim 5, wherein SEQ ID Nos. 51-55detect stage 1 grade 2 breast cancer. 